import gradio as gr import os import time from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig import torch from threading import Thread import logging import spaces from functools import lru_cache # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Set an environment variable HF_TOKEN = os.environ.get("HF_TOKEN", None) DESCRIPTION = '''

ContenteaseAI custom trained model

''' LICENSE = """

--- For more information, visit our [website](https://contentease.ai). """ PLACEHOLDER = """

ContenteaseAI Custom AI trained model

Enter the text extracted from the PDF:

""" css = """ h1 { text-align: center; display: block; } """ # Load the tokenizer and model with quantization model_id = "meta-llama/Meta-Llama-3-8B-Instruct" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) @lru_cache(maxsize=1) def load_model_and_tokenizer(): try: start_time = time.time() logger.info("Loading tokenizer...") tokenizer = AutoTokenizer.from_pretrained(model_id) logger.info("Loading model...") model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", quantization_config=bnb_config, torch_dtype=torch.bfloat16 ) model.generation_config.pad_token_id = tokenizer.pad_token_id end_time = time.time() logger.info(f"Model and tokenizer loaded successfully in {end_time - start_time} seconds.") return model, tokenizer except Exception as e: logger.error(f"Error loading model or tokenizer: {e}") raise try: model, tokenizer = load_model_and_tokenizer() except Exception as e: logger.error(f"Failed to load model and tokenizer: {e}") raise terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] SYS_PROMPT = """ Extract all relevant keywords and add quantity from the following text and format the result in nested JSON, ignoring personal details and focusing only on the scope of work as shown in the example: Good JSON example: {'lobby': {'frcm': {'replace': {'carpet': 1, 'carpet_pad': 1, 'base': 1, 'window_treatments': 1, 'artwork_and_decorative_accessories': 1, 'portable_lighting': 1, 'upholstered_furniture_and_decorative_pillows': 1, 'millwork': 1} } } } Bad JSON example: {'lobby': { 'frcm': { 'replace': [ 'carpet', 'carpet_pad', 'base', 'window_treatments', 'artwork_and_decorative_accessories', 'portable_lighting', 'upholstered_furniture_and_decorative_pillows', 'millwork'] } } } Make sure to fetch details from the provided text and ignore unnecessary information. The response should be in JSON format only, without any additional comments. """ def chunk_text(text, chunk_size=5000): """ Splits the input text into chunks of specified size. Args: text (str): The input text to be chunked. chunk_size (int): The size of each chunk in tokens. Returns: list: A list of text chunks. """ words = text.split() chunks = [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)] return chunks def combine_responses(responses): """ Combines the responses from all chunks into a final output string. Args: responses (list): A list of responses from each chunk. Returns: str: The combined output string. """ combined_output = " ".join(responses) return combined_output def generate_response_for_chunk(chunk, history, temperature, max_new_tokens): start_time = time.time() conversation = [{"role": "system", "content": SYS_PROMPT}] for user, assistant in history: conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) conversation.append({"role": "user", "content": chunk}) input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids=input_ids, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, eos_token_id=terminators, pad_token_id=tokenizer.eos_token_id ) if temperature == 0: generate_kwargs['do_sample'] = False t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() outputs = [] for text in streamer: outputs.append(text) end_time = time.time() logger.info(f"Time taken for generating response for a chunk: {end_time - start_time} seconds") return "".join(outputs) @spaces.GPU(duration=110) def chat_llama3_8b(message: str, history: list, temperature: float, max_new_tokens: int): """ Generate a streaming response using the llama3-8b model with chunking. Args: message (str): The input message. history (list): The conversation history used by ChatInterface. temperature (float): The temperature for generating the response. max_new_tokens (int): The maximum number of new tokens to generate. Returns: str: The generated response. """ try: start_time = time.time() chunks = chunk_text(message) responses = [] for chunk in chunks: response = generate_response_for_chunk(chunk, history, temperature, max_new_tokens) responses.append(response) final_output = combine_responses(responses) end_time = time.time() logger.info(f"Total time taken for generating response: {end_time - start_time} seconds") yield final_output except Exception as e: logger.error(f"Error generating response: {e}") yield "An error occurred while generating the response. Please try again." # Gradio block chatbot = gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface') with gr.Blocks(fill_height=True, css=css) as demo: gr.Markdown(DESCRIPTION) gr.ChatInterface( fn=chat_llama3_8b, chatbot=chatbot, fill_height=True, additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), additional_inputs=[ gr.Slider(minimum=0, maximum=1, step=0.1, value=0.95, label="Temperature", render=False), gr.Slider(minimum=128, maximum=2000, step=1, value=700, label="Max new tokens", render=False), ] ) gr.Markdown(LICENSE) if __name__ == "__main__": try: demo.launch(show_error=True) except Exception as e: logger.error(f"Error launching Gradio demo: {e}")